Method for concealing data and data obfuscation device using the same

ABSTRACT

A method for concealing original data to protect personal information is provided. The method includes steps of: a data obfuscation device (a) if the original data is acquired, inputting the original data or its modified data into a learning network, and allowing the learning network to (i) apply a network operation to the original data or the modified data using learned parameters of the learning network and thus to (ii) output characteristic information on the original data or the modified data; and (b) updating the original data or the modified data via backpropagation using part of (i) 1-st losses calculated by referring to the characteristic information and its corresponding 1-st ground truth, and (ii) 2-nd losses calculated by referring to (ii-1) a task specific output generated by using the characteristic information and (ii-2) a 2-nd ground truth corresponding to the task specific output, to thereby generate obfuscated data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. application Ser. No.16/513,715, filed Jul. 17, 2019, which claims priority to KR10-2018-0086929, filed Jul. 26, 2018, the entire contents of each areincorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to a method for concealing data and adata obfuscation device using the same; and more particularly, to themethod for concealing identification information included in the data tobe inputted into a learning network and the data obfuscation deviceusing the same.

BACKGROUND OF THE DISCLOSURE

Big data refers to data including all of unstructured data andsemi-structured data not utilized so far, like e-commerce data,metadata, web log data, radio frequency identification (RFID) data,sensor network data, social network data, data of Internet text anddocuments, Internet search indexing data, as well as all of structureddata used by conventional enterprises or public institutions. Data assuch is referred to as big data in the sense that common software toolsand computer systems cannot handle such a huge volume of data.

And, although such big data may be insignificant by itself, it can beuseful for generation of new data, judgment, or prediction in variousfields through machine learning on patterns and the like.

Recently, due to the strengthening of a personal information protectionact, it is required to delete information that can be used foridentifying individuals from the data or to acquire consent of theindividuals in order to trade or share such big data. However, it is noteasy to check if a large amount of big data includes information thatcan be used for identifying the individuals, and it is impossible toobtain the consent of the individuals. Therefore, various techniques forsuch purposes are emerging.

As an example of a related prior art, a technique is disclosed in KoreanPatent Registration No. 1861520. According to this technique, aface-concealing method is provided which includes a detection step ofdetecting a facial region of a person in an input image to betransformed, a first concealing step of transforming the detected facialregion into a distorted first image that does not have a facial shape ofthe person so that the person in the input image is prevented from beingidentified, and a second concealing step of generating a second imagehaving a predetermined facial shape based on the first image,transforming the first image into the second image, in the input image,where the second image is generated to have a facial shape differentfrom that of the facial region detected in the detection step.

However, according to conventional techniques as well as the techniquedescribed above, whether identification information such as faces, text,etc. is included in the data is determined, and at least one portioncorresponding to the identification information is masked or blurred,thus machine learning cannot utilize such information due to damage tooriginal data, and in some cases, the data even contains unexpectedidentification information and the unexpected identification informationcannot be concealed, e.g., anonymized. In particular, a conventionalsecurity camera performs an anonymizing process by blurring all pixelshaving a change between frames in a video image, and when theanonymizing process is performed in this manner, critical informationsuch as facial expression of an anonymized face becomes different frominformation contained in an original video image, and the personalidentification information missing during face detection may remain onthe original video image. Also, the blurred video image may be revertedto the original image using one of conventional video deblurringtechniques.

Accordingly, the inventors of the present disclosure propose a methodfor generating obfuscated data such that the obfuscated data isdifferent from the original data while an output result of inputting theoriginal data into a machine learning model and an output result ofinputting the obfuscated data into the learning model are same orsimilar to each other.

SUMMARY OF THE DISCLOSURE

It is an object of the present disclosure to solve all theaforementioned problems.

It is another object of the present disclosure to perform anonymizationin a simple and accurate way, since processes of finding personalidentification information in data are eliminated.

It is still another object of the present disclosure to protect privacyand security of original data by generating irreversibly obfuscated andanonymized data from the original data.

It is still yet another object of the present disclosure to generatedata recognized as similar or same by a computer, but recognized asdifferent by a human.

It is still yet another object of the present disclosure to stimulate abig data trade market.

In accordance with one aspect of the present disclosure, there isprovided a method for concealing original data to protect personalinformation, including steps of: (a) a data obfuscation device, if theoriginal data is acquired, inputting the original data or its modifieddata into a learning network, and allowing the learning network to (i)apply a network operation to the original data or the modified datausing one or more learned parameters of the learning network and thus to(ii) output characteristic information on the original data or themodified data; and (b) the data obfuscation device updating the originaldata or the modified data via backpropagation using at least part of (i)one or more 1-st losses calculated by referring to the characteristicinformation and its corresponding at least one 1-st ground truth, and(ii) one or more 2-nd losses calculated by referring to (ii-1) at leastone task specific output generated by using the characteristicinformation and (ii-2) at least one 2-nd ground truth corresponding tothe task specific output, to thereby generate obfuscated datacorresponding to the original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the step of (a), the dataobfuscation device inputs the original data or the modified data intothe 1-st learning network, and allows the 1-st learning network to (i)apply a network operation to the original data or the modified datausing the 1-st learned parameters of the 1-st learning network and thusto (ii) output 1-st characteristic information on the original data orthe modified data, wherein, at the step of (b), the data obfuscationdevice updates the original data or the modified data viabackpropagation using at least part of (i) one or more (1_1)-st lossescalculated by referring to the 1-st characteristic information and itscorresponding at least one (1_1)-st ground truth, and (ii) one or more(2_1)-st losses calculated by referring to (ii-1) at least one 1-st taskspecific output generated by using the 1-st characteristic informationand (ii-2) at least one (2_1)-st ground truth corresponding to the 1-sttask specific output, to thereby generate 1-st obfuscated datacorresponding to the original data or the modified data, and wherein,while increasing an integer k from 2 to n, the data obfuscation devicerepeats processes of (i) inputting (k-1)-th obfuscated data into a k-thlearning network, and allowing the k-th learning network to (i-1) applya network operation to the (k-1)-th obfuscated data using one or morek-th learned parameters of the k-th learning network and thus to (i-2)output k-th characteristic information on the (k-1)-th obfuscated data,and (ii) updating the (k-1)-th obfuscated data via backpropagation usingat least part of (ii-1) one or more (1_k)-th losses calculated byreferring to the k-th characteristic information and its correspondingat least one (1_k)-th ground truth and (ii-2) one or more (2_k)-thlosses calculated by referring to at least one k-th task specific valueand at least one (2_k)-th ground truth wherein the k-th task specificvalue is generated by using the k-th characteristic information and the(2_k)-th ground truth corresponds to the k-th task specific value, tothereby generate k-th obfuscated data corresponding to the (k-1)-thobfuscated data, and wherein, as a result of the repeated processes,n-th obfuscated data which are the obfuscated data corresponding to theoriginal data are generated.

As one example, the data obfuscation device updates the original data orthe modified data via backpropagation using at least part of (i) a 1-staverage loss which is an average over the (1_1)-st losses to (1_n)-thlosses, (ii) a 2-nd average loss which is an average over the (2_1)-stlosses to (2_n)-th losses and (iii) a 3-rd average loss which is anaverage over a 1-st sum loss to an n-th sum loss wherein each of the1-st sum loss to the n-th sum loss is each piecewise summation of the(1_1)-st losses to the (1_n)-th losses and the (2_1)-st losses to the(2_n)-th losses corresponding to the (1_1)-st losses to the (1_n)-thlosses, to thereby generate the obfuscated data.

As one example, at the step of (b), the data obfuscation devicemaintains the learned parameters of the learning network at constantvalues during the backpropagation using at least part of the 1-st lossesand the 2-nd losses.

As one example, at the step of (b), the data obfuscation device acquiresat least one loss gradient for minimizing at least part of the 1-stlosses and the 2-nd losses, and backpropagates the loss gradient to theoriginal data or the modified data.

As one example, at the step of (a), the data obfuscation devicegenerates the modified data corresponding to the original data byperforming at least one of a process of adding a random noise created bya random noise generating network to the original data, a process ofblurring the original data and, a process of changing a resolution ofthe original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the step of (a), the dataobfuscation device inputs the original data or the modified data intoeach of the 1-st learning network to the n-th learning network, andallows each of the 1-st learning network to the n-th learning network to(i) apply its corresponding network operation to the original data orthe modified data using respectively the 1-st learned parameters to then-th learned parameters of the 1-st learning network to the n-thlearning network, and thus to (ii) output each piece of 1-stcharacteristic information to n-th characteristic information on theoriginal data or the modified data, and wherein, at the step of (b), thedata obfuscation device updates the original data or the modified datavia backpropagation using at least part of (i) one of the 1-st losseswhich is an average over (1_1)-st losses to (1_n)-th losses wherein the(1_1)-st losses to the (1_n)-th losses are calculated by referring tothe 1-st characteristic information to the n-th characteristicinformation and at least one (1_1)-st ground truth to at least one(1_n)-th ground truth respectively corresponding to the 1-stcharacteristic information to the n-th characteristic information, (ii)one of the 2-nd losses which is an average over (2_1)-st losses to(2_n)-th losses wherein the (2_1)-st losses to the (2_n)-th losses arecalculated by referring to a 1-st task specific output to an n-th taskspecific output generated by using each piece of the 1-st characteristicinformation to the n-th characteristic information and by furtherreferring to at least one (2_1)-st ground truth to at least one (2_n)-thground truth respectively corresponding to the 1-st task specific outputto the n-th task specific output, and (iii) a 3-rd loss which is anaverage over a 1-st sum loss to an n-th sum loss wherein each of the1-st sum loss to the n-th sum loss is each piecewise summation of the(1_1)-st losses to the (1_n)-th losses and the (2_1)-st losses to the(2_n)-th losses corresponding to the (1_1)-st losses to the (1_n)-thlosses, to thereby generate the obfuscated data corresponding to theoriginal data.

In accordance with another aspect of the present disclosure, there isprovided a method for concealing original data to protect personalinformation, including steps of: (a) a data obfuscation device, if theoriginal data is acquired, modifying the original data, to therebygenerate modified data; (b) the data obfuscation device, (i) inputtingthe original data into a learning network, and allowing the learningnetwork to (i-1) apply a network operation to the original data usingone or more learned parameters of the learning network and thus to (i-2)output 1-st characteristic information on the original data, and (ii)inputting the modified data into the learning network, and allowing thelearning network to (ii-1) apply a network operation to the modifieddata using the learned parameters and thus to (ii-2) output 2-ndcharacteristic information on the modified data; and (c) the dataobfuscation device updating the original data or the modified data viabackpropagation using one or more data losses created by referring to atleast part of (i) one or more 1-st losses calculated by referring to the1-st characteristic information and the 2-nd characteristic information,and (ii) one or more 2-nd losses calculated by referring to (ii-1) atleast one task specific output generated by using the 2-ndcharacteristic information and (ii-2) at least one ground truthcorresponding to the task specific output, to thereby generateobfuscated data corresponding to the original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the step of (b), the dataobfuscation device performs processes of (i) inputting the original datainto the 1-st learning network, and allowing the 1-st learning networkto (i-1) apply a network operation to the original data using one ormore 1-st learned parameters of the 1-st learning network and thus to(i-2) output (1_1)-st characteristic information on the original data,and (ii) inputting the modified data into the 1-st learning network, andallowing the 1-st learning network to (ii-1) apply a network operationto the modified data using the 1-st learned parameters and thus to(ii-2) output (2_1)-st characteristic information on the modified data,wherein, at the step of (c), the data obfuscation device updates themodified data via backpropagation using one or more 1-st data lossescalculated by referring to at least part of (i) one or more (1_1)-stlosses created by referring to the (1_1)-st characteristic informationand the (2_1)-st characteristic information, and (ii) one or more(2_1)-st losses created by referring to (ii-1) at least one 1-st taskspecific output generated by using the (2_1)-st characteristicinformation and (ii-2) at least one 1-st ground truth corresponding tothe 1-st task specific output, to thereby generate 1-st obfuscated datacorresponding to the modified data, and wherein, while increasing aninteger k from 2 to n, the data obfuscation device repeats processes of(i) inputting the original data into a k-th learning network, andallowing the k-th learning network to (i-1) apply a network operation tothe original data using one or more k-th learned parameters of the k-thlearning network and thus to (i-2) output (1_k)-th characteristicinformation on the original data, (ii) inputting (k-1)-th obfuscateddata into the k-th learning network, and allowing the k-th learningnetwork to (ii-1) apply a network operation to the (k-1)-th obfuscateddata using the k-th learned parameters and thus to (ii-2) output(2_k)-th characteristic information on the (k-1)-th obfuscated data, and(iii) updating the (k-1)-th obfuscated data by backpropagation using oneor more k-th data losses created by referring to at least part of(iii-1) one or more (1_k)-th losses calculated by referring to the(1_k)-th characteristic information and the (2_k)-th characteristicinformation and (iii-2) one or more (2_k)-th losses calculated byreferring to at least one k-th task specific output generated by usingthe (2_k)-th characteristic information and at least one k-th groundtruth corresponding to the k-th task specific output, to generate k-thobfuscated data corresponding to the (k-1)-th obfuscated data, andwherein, as a result of the repeated processes, n-th obfuscated datawhich are the obfuscated data corresponding to the original data aregenerated.

As one example, the data obfuscation device updates the original data orthe modified data via backpropagation using at least one average dataloss which is an average over the 1-st data losses to the n-th datalosses, to thereby generate the obfuscated data.

As one example, the data obfuscation device (i) calculates, as the 1-stsub losses, at least one average loss which is an average over the(1_1)-st losses to the (1_n)-th losses, and (ii) calculates the 2-nd sublosses by referring to the specific characteristic information and anaverage over the (2_1)-st characteristic information to the (2_n)-thcharacteristic information.

As one example, at the step of (c), the data obfuscation devicemaintains the learned parameters of the learning network at constantvalues during the backpropagation using the data losses.

As one example, at the step of (a), the data obfuscation devicegenerates the modified data corresponding to the original data byperforming at least one of a process of adding a random noise created bya random noise generating network to the original data, a process ofblurring of the original data, and a process of changing a resolution ofthe original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the step of (b), the dataobfuscation device (i) inputs the original data and the modified datainto each of the 1-st learning network to the n-th learning network, and(ii) allows each of the 1-st learning network to the n-th learningnetwork to (ii-1) apply its corresponding network operation to theoriginal data and the modified data using respectively the 1-st learnedparameters to the n-th learned parameters of the 1-st learning networkto the n-th learning network, thus to (ii-2) output each piece of(1_1)-st characteristic information to (1_n)-th characteristicinformation on the original data using respectively the 1-st learnedparameters to the n-th learned parameters, and (ii-3) output each pieceof (2_1)-st characteristic information to (2_n)-th characteristicinformation on the modified data, wherein, at the step of (c), the dataobfuscation device updates the original data or the modified data viabackpropagation using at least part of (i) one of the 1-st losses whichis an average over (1_1)-st losses to (1_n)-th losses wherein the(1_1)-st losses to the (1_n)-th losses are calculated by referring tothe (1_1)-st characteristic information to the (1_n)-th characteristicinformation and the (2_1)-st characteristic information to the (2_n)-thcharacteristic information corresponding to the (1_1)-st characteristicinformation to the (1_n)-th characteristic information, (ii) one of the2-nd losses which is an average over (2_1)-st losses to (2_n)-th losseswherein the (2_1)-st losses to the (2_n)-th losses are calculated byreferring to a 1-st task specific output to an n-th task specific outputgenerated by using each piece of the (2_1)-st characteristic informationto the (2_n)-th characteristic information and by further referring toat least one 1-st ground truth to at least one n-th ground truthrespectively corresponding to the 1-st task specific output to the n-thtask specific output, and (iii) a 3-rd loss which is an average over a1-st sum loss to an n-th sum loss wherein each of the 1-st sum loss tothe n-th sum loss is each piecewise summation of the (1_1)-st losses tothe (1_n)-th losses and the (2_1)-st losses to the (2_n)-th lossescorresponding to the (1_1)-st losses to the (1_n)-th losses, to therebygenerate the obfuscated data corresponding to the original data.

In accordance with still another aspect of the present disclosure, thereis provided a data obfuscation device for concealing original data toprotect personal information including: at least one memory that storesinstructions; and at least one processor configured to execute theinstructions to perform or support another device to perform processesof: (I) if the original data is acquired, inputting the original data orits modified data into a learning network, and allowing the learningnetwork to (i) apply a network operation to the original data or themodified data using one or more learned parameters of the learningnetwork and thus to (ii) output characteristic information on theoriginal data or the modified data, and (II) updating the original dataor the modified data via backpropagation using at least part of (i) oneor more 1-st losses calculated by referring to the characteristicinformation and its corresponding at least one 1-st ground truth, and(ii) one or more 2-nd losses calculated by referring to (ii-1) at leastone task specific output generated by using the characteristicinformation and (ii-2) at least one 2-nd ground truth corresponding tothe task specific output, to thereby generate obfuscated datacorresponding to the original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (I), the processorinputs the original data or the modified data into the 1-st learningnetwork, and allows the 1-st learning network to (i) apply a networkoperation to the original data or the modified data using the 1-stlearned parameters of the 1-st learning network and thus to (ii) output1-st characteristic information on the original data or the modifieddata, wherein, at the process of (II), the processor updates theoriginal data or the modified data via backpropagation using at leastpart of (i) one or more (1_1)-st losses calculated by referring to the1-st characteristic information and its corresponding at least one(1_1)-st ground truth, and (ii) one or more (2_1)-st losses calculatedby referring to (ii-1) at least one 1-st task specific output generatedby using the 1-st characteristic information and (ii-2) at least one(2_1)-st ground truth corresponding to the 1-st task specific output, tothereby generate 1-st obfuscated data corresponding to the original dataor the modified data, and wherein, while increasing an integer k from 2to n, the processor repeats processes of (i) inputting (k-1)-thobfuscated data into a k-th learning network, and allowing the k-thlearning network to (i-1) apply a network operation to the (k-1)-thobfuscated data using one or more k-th learned parameters of the k-thlearning network and thus to (i-2) output k-th characteristicinformation on the (k-1)-th obfuscated data, and (ii) updating the(k-1)-th obfuscated data via backpropagation using at least part of(ii-1) one or more (1_k)-th losses calculated by referring to the k-thcharacteristic information and its corresponding at least one (1_k)-thground truth and (ii-2) one or more 2_k)-th losses calculated byreferring to at least one k-th task specific value and at least one2_k)-th ground truth wherein the k-th task specific value is generatedby using the k-th characteristic information and the 2_k)-th groundtruth corresponds to the k-th task specific value, to thereby generatek-th obfuscated data corresponding to the (k-1)-th obfuscated data, andwherein, as a result of the repeated processes, n-th obfuscated datawhich are the obfuscated data corresponding to the original data aregenerated.

As one example, the processor updates the original data or the modifieddata via backpropagation using at least part of (i) a 1-st average losswhich is an average over the (1_1)-st losses to (1_n)-th losses, (ii) a2-nd average loss which is an average over the (2_1)-st losses to(2_n)-th losses and (iii) a 3-rd average loss which is an average over a1-st sum loss to an n-th sum loss wherein each of the 1-st sum loss tothe n-th sum loss is each piecewise summation of the (1_1)-st losses tothe (1_n)-th losses and the (2_1)-st losses to the (2_n)-th lossescorresponding to the (1_1)-st losses to the (1_n)-th losses, to therebygenerate the obfuscated data.

As one example, at the process of (II), the processor maintains thelearned parameters of the learning network at constant values during thebackpropagation using at least part of the 1-st losses and the 2-ndlosses.

As one example, at the process of (II), the processor acquires at leastone loss gradient for minimizing at least part of the 1-st losses andthe 2-nd losses, and backpropagates the loss gradient to the originaldata or the modified data.

As one example, at the process of (I), the processor generates themodified data corresponding to the original data by performing at leastone of a process of adding a random noise created by a random noisegenerating network to the original data, a process of blurring theoriginal data and, a process of changing a resolution of the originaldata.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (I), the processorinputs the original data or the modified data into each of the 1-stlearning network to the n-th learning network, and allows each of the1-st learning network to the n-th learning network to (i) apply itscorresponding network operation to the original data or the modifieddata using respectively the 1-st learned parameters to the n-th learnedparameters of the 1-st learning network to the n-th learning network,and thus to (ii) output each piece of 1-st characteristic information ton-th characteristic information on the original data or the modifieddata using respectively the 1-st learned parameters to the n-th learnedparameters, and wherein, at the process of (II), the processor updatesthe original data or the modified data via backpropagation using atleast part of (i) one of the 1-st losses which is an average over(1_1)-st losses to (1_n)-th losses wherein the (1_1)-st losses to the(1_n)-th losses are calculated by referring to the 1-st characteristicinformation to the n-th characteristic information and at least one(1_1)-st ground truth to at least one (1_n)-th ground truth respectivelycorresponding to the 1-st characteristic information to the n-thcharacteristic information, (ii) one of the 2-nd losses which is anaverage over (2_1)-st losses to (2_n)-th losses wherein the (2_1)-stlosses to the (2_n)-th losses are calculated by referring to a 1-st taskspecific output to an n-th task specific output generated by using eachpiece of the 1-st characteristic information to the n-th characteristicinformation and by further referring to at least one (2_1)-st groundtruth to at least one (2_n)-th ground truth respectively correspondingto the 1-st task specific output to the n-th task specific output, and(iii) a 3-rd loss which is an average over a 1-st sum loss to an n-thsum loss wherein each of the 1-st sum loss to the n-th sum loss is eachpiecewise summation of the (1_1)-st losses to the (1_n)-th losses andthe (2_1)-st losses to the (2_n)-th losses corresponding to the (1_1)-stlosses to the (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.

In accordance with still yet another aspect of the present disclosure,there is provided a data obfuscation device for concealing original datato protect personal information, including: at least one memory thatstores instructions; and at least one processor configured to executethe instructions to perform or support another device to performprocesses of: (I) if the original data is acquired, modifying theoriginal data, to thereby generate modified data, (II) (i) inputting theoriginal data into a learning network, and allowing the learning networkto (i-1) apply a network operation to the original data using one ormore learned parameters of the learning network and thus to (i-2) output1-st characteristic information on the original data, and (ii) inputtingthe modified data into the learning network, and allowing the learningnetwork to (ii-1) apply a network operation to the modified data usingthe learned parameters and thus to (ii-2) output 2-nd characteristicinformation on the modified data, and (III) updating the original dataor the modified data via backpropagation using one or more data lossescreated by referring to at least part of (i) one or more 1-st lossescalculated by referring to the 1-st characteristic information and the2-nd characteristic information, and (ii) one or more 2-nd lossescalculated by referring to (ii-1) at least one task specific outputgenerated by using the 2-nd characteristic information and (ii-2) atleast one ground truth corresponding to the task specific output, tothereby generate obfuscated data corresponding to the original data.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (II), the processorperforms processes of (i) inputting the original data into the 1-stlearning network, and allowing the 1-st learning network to (i-1) applya network operation to the original data using one or more 1-st learnedparameters of the 1-st learning network and thus to (i-2) output(1_1)-st characteristic information on the original data, and (ii)inputting the modified data into the 1-st learning network, and allowingthe 1-st learning network to (ii-1) apply a network operation to themodified data using the 1-st learned parameters and thus to (ii-2)output (2_1)-st characteristic information on the modified data,wherein, at the process of (c), the processor updates the modified datavia backpropagation using one or more 1-st data losses calculated byreferring to at least part of (i) one or more (1_1)-st losses created byreferring to the (1_1)-st characteristic information and the (2_1)-stcharacteristic information, and (ii) one or more (2_1)-st losses createdby referring to (ii-1) at least one 1-st task specific output generatedby using the (2_1)-st characteristic information and (ii-2) at least one1-st ground truth corresponding to the 1-st task specific output, tothereby generate 1-st obfuscated data corresponding to the modifieddata, and wherein, while increasing an integer k from 2 to n, theprocessor repeats processes of (i) inputting the original data into ak-th learning network, and allowing the k-th learning network to (i-1)apply a network operation to the original data using one or more k-thlearned parameters of the k-th learning network and thus to (i-2) output(1_k)-th characteristic information on the original data, (ii) inputting(k-1)-th obfuscated data into the k-th learning network, and allowingthe k-th learning network to (ii-1) apply a network operation to the(k-1)-th obfuscated data using the k-th learned parameters and thus to(ii-2) output 2_k)-th characteristic information on the (k-1)-thobfuscated data, and (iii) updating the (k-1)-th obfuscated data bybackpropagation using one or more k-th data losses created by referringto at least part of (iii-1) one or more (1_k)-th losses calculated byreferring to the (1_k)-th characteristic information and the 2_k)-thcharacteristic information and (iii-2) one or more 2_k)-th lossescalculated by referring to at least one k-th task specific outputgenerated by using the 2_k)-th characteristic information and at leastone k-th ground truth corresponding to the k-th task specific output, togenerate k-th obfuscated data corresponding to the (k-1)-th obfuscateddata, and wherein, as a result of the repeated processes, n-thobfuscated data which are the obfuscated data corresponding to theoriginal data are generated.

As one example, the processor updates the original data or the modifieddata via backpropagation using at least one average data loss which isan average over the 1-st data losses to the n-th data losses, to therebygenerate the obfuscated data.

As one example, the processor (i) calculates, as the 1-st sub losses, atleast one average loss which is an average over the (1_1)-st losses tothe (1_n)-th losses, and (ii) calculates the 2-nd sub losses byreferring to the specific characteristic information and an average overthe (2_1)-st characteristic information to the (2_n)-th characteristicinformation.

As one example, at the process of (III), the processor maintains thelearned parameters of the learning network at constant values during thebackpropagation using the data losses.

As one example, at the process of (I), the processor generates themodified data corresponding to the original data by performing at leastone of a process of adding a random noise created by a random noisegenerating network to the original data, a process of blurring of theoriginal data, and a process of changing a resolution of the originaldata.

As one example, the learning network includes a 1-st learning network toan n-th learning network respectively having one or more 1-st learnedparameters to one or more n-th learned parameters wherein n is aninteger greater than 0, wherein, at the process of (II), the processor(i) inputs the original data and the modified data into each of the 1-stlearning network to the n-th learning network, and (ii) allows each ofthe 1-st learning network to the n-th learning network to (ii-1) applyits corresponding network operation to the original data and themodified data using respectively the 1-st learned parameters to the n-thlearned parameters of the 1-st learning network to the n-th learningnetwork, thus to (ii-2) output each piece of (1_1)-st characteristicinformation to (1_n)-th characteristic information on the original datausing respectively the 1-st learned parameters to the n-th learnedparameters, and (ii-3) output each piece of (2_1)-st characteristicinformation to (2_n)-th characteristic information on the modified data,wherein, at the process of (III), the processor updates the originaldata or the modified data via backpropagation using at least part of (i)one of the 1-st losses which is an average over (1_1)-st losses to(1_n)-th losses wherein the (1_1)-st losses to the (1_n)-th losses arecalculated by referring to the (1_1)-st characteristic information tothe (1_n)-th characteristic information and the (2_1)-st characteristicinformation to the (2_n)-th characteristic information corresponding tothe (1_1)-st characteristic information to the (1_n)-th characteristicinformation, (ii) one of the 2-nd losses which is an average over(2_1)-st losses to (2_n)-th losses wherein the (2_1)-st losses to the(2_n)-th losses are calculated by referring to a 1-st task specificoutput to an n-th task specific output generated by using each piece ofthe (2_1)-st characteristic information to the (2_n)-th characteristicinformation and by further referring to at least one 1-st ground truthto at least one n-th ground truth respectively corresponding to the 1-sttask specific output to the n-th task specific output, and (iii) a 3-rdloss which is an average over a 1-st sum loss to an n-th sum losswherein each of the 1-st sum loss to the n-th sum loss is each piecewisesummation of the (1_1)-st losses to the (1_n)-th losses and the (2_1)-stlosses to the (2_n)-th losses corresponding to the (1_1)-st losses tothe (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.

In addition, recordable media that are readable by a computer forstoring a computer program to execute the method of the presentdisclosure is further provided.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The above and other objects and features of the present disclosure willbecome apparent from the following description of preferred embodimentsgiven in conjunction with the accompanying drawings, in which:

FIG. 1 is a drawing schematically illustrating a data obfuscation devicefor concealing data in accordance with one example embodiment of thepresent disclosure.

FIG. 2 is a drawing schematically illustrating a method for concealingthe data in accordance with one example embodiment of the presentdisclosure.

FIG. 3 is a drawing schematically illustrating another method forconcealing the data in accordance with one example embodiment of thepresent disclosure.

FIG. 4 is a drawing schematically illustrating a method for concealingthe data in accordance with another example embodiment of the presentdisclosure.

FIG. 5 is a drawing schematically illustrating another method forconcealing the data in accordance with another example embodiment of thepresent disclosure.

FIG. 6 is a drawing schematically illustrating a method for concealingthe data in accordance with still another example embodiment of thepresent disclosure.

FIG. 7 is a drawing schematically illustrating another method forconcealing the data in accordance with still another example embodimentof the present disclosure.

FIGS. 8A and 8B are drawings schematically illustrating original dataand concealed original data in accordance with the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following detailed description, reference is made to theaccompanying drawings that show, by way of illustration, specificembodiments in which the present disclosure may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the present disclosure. It is to be understoodthat the various embodiments of the present disclosure, althoughdifferent, are not necessarily mutually exclusive. For example, aparticular feature, structure, or characteristic described herein may beimplemented as being changed from an embodiment to other embodimentswithout departing from the spirit and scope of the present disclosure.In addition, it is to be understood that the position or arrangement ofindividual elements within each embodiment may be modified withoutdeparting from the spirit and scope of the present disclosure. Thefollowing detailed description is, therefore, not to be taken in alimiting sense, and the scope of the present disclosure is described asincluding the appended claims, along with the full range of equivalentsto which the claims are entitled. In the drawings, like numerals referto the same or similar components throughout the several aspects.

Besides, in the detailed description and claims of the presentdisclosure, a term “include” and its variations are not intended toexclude other technical features, additions, components or steps. Otherobjects, benefits and features of the present disclosure will berevealed to one skilled in the art, partially from the specification andpartially from the implementation of the present disclosure. Thefollowing examples and drawings will be provided as examples but theyare not intended to limit the present disclosure.

To allow those skilled in the art to carry out the present disclosureeasily, the example embodiments of the present disclosure will beexplained in detail as shown below by referring to attached drawings.

FIG. 1 is a drawing schematically illustrating a data obfuscation devicefor concealing data in accordance with one example embodiment of thepresent disclosure.

By referring to FIG. 1, the data obfuscation device 100 in accordancewith one example embodiment of the present disclosure may include amemory 110 for storing instructions to generate obfuscated data asconcealed data, e.g., as anonymized data, and a processor 120 capable ofperforming processes for generating the obfuscated data corresponding tothe original data according to the instructions in the memory 110.Herein, an output result calculated by using the obfuscated data may besame or similar to an output result calculated by using original data.

Specifically, the data obfuscation device 100 may typically achieve adesired system performance by using combinations of at least onecomputing device and at least one computer software, e.g., a computerprocessor, a memory, a storage, an input device, an output device, orany other conventional computing components, an electronic communicationdevice such as a router or a switch, an electronic information storagesystem such as a network-attached storage (NAS) device and a storagearea network (SAN) as the computing device and any instructions thatallow the computing device to function in a specific way as the computersoftware.

Also, the processors of such devices may include hardware configurationof MPU (Micro Processing Unit) or CPU (Central Processing Unit), cachememory, data bus, etc. Additionally, the computing device may furtherinclude OS and software configuration of applications that achievespecific purposes.

Such description of the computing device does not exclude an integrateddevice including any combination of a processor, a memory, a medium, orany other computing components for implementing the present disclosure.

Meanwhile, according to the instructions stored in the memory 110, ifthe original data is acquired, the processor 120 of the data obfuscationdevice 100 may perform processes of (i) inputting the original data orits modified data into a learning network, and allowing the learningnetwork to (i-1) apply a network operation to the original data or themodified data using one or more learned parameters of the learningnetwork and thus to (i-2) output characteristic information on theoriginal data or the modified data, and (ii) updating the original dataor the modified data via backpropagation using at least part of (ii-1)one or more 1-st losses calculated by referring to the characteristicinformation and its corresponding at least one 1-st ground truth, and(ii-2) one or more 2-nd losses calculated by referring to at least onetask specific output generated by using the characteristic informationand at least one 2-nd ground truth corresponding to the the taskspecific output, to thereby generate obfuscated data corresponding tothe original data. Herein, the learning network may be a network, havingits learned parameters, whose learning has been completed.

Also, according to the instructions stored in the memory 110, if theoriginal data is acquired, the processor 120 of the data obfuscationdevice 100 may perform processes of (i) modifying the original data togenerate the modified data, (ii) inputting the original data into thelearning network, and allowing the learning network to (ii-1) apply anetwork operation to the original data using the learned parameters ofthe learning network and thus to (ii-2) output 1-st characteristicinformation on the original data, and (iii) inputting the modified datainto the learning network, and allowing the learning network to (iii-1)apply a network operation to the modified data using the learnedparameters and thus to (iii-2) output 2-nd characteristic information onthe modified data. Thereafter, the data obfuscation device may performprocesses of updating the original data or the modified data viabackpropagation using one or more data losses calculated by referring toat least part of (i) one or more 1-st losses created by referring to the1-st characteristic information and the 2-nd characteristic information,and (ii) one or more 2-nd losses created by referring to (ii-1) the taskspecific output generated by using the 2-nd characteristic informationand (ii-2) the ground truth corresponding to the task specific output,to thereby generate the obfuscated data corresponding to the originaldata.

A method for concealing the original data to protect personalinformation by using the data obfuscation device 100 in accordance withone example embodiment of the present disclosure is described byreferring to FIGS. 2 to 8 as follows.

FIG. 2 is a drawing schematically illustrating a method for generatingthe obfuscated data as a concealed data, e.g., an anonymized data. Anoutput result calculated using the obfuscated data may be same orsimilar to an output result calculated using the original data, by usingthe learning network 130 having its learned parameters, that is, thelearning network 130 having its parameters adjusted so that a desiredresult is outputted from training data in accordance with one exampleembodiment of the present disclosure.

First, if the original data x is acquired, the data obfuscation device100 may perform processes of inputting the original data x into thelearning network 130, and allowing the learning network 130 to (i) applya network operation to the original data using one or more learnedparameters of the learning network 130 and thus to (ii) outputcharacteristic information y on the original data.

That is, according to the instructions stored in the memory 110 of thedata obfuscation device 100, if the original data x for concealment isacquired, the data obfuscation device 100, i.e., the processor 120, mayinput the original data x into the learning network 130. Herein, thelearning network 130 may include a machine learning network, but thescope of the present disclosure is not limited thereto, and may includeany learning network capable of generating an output by applying anetwork operation using learned parameters to the inputted original datax. And, the machine learning network may include at least one of ak-Nearest Neighbors, a Linear Regression, a Logistic Regression, aSupport Vector Machine (SVM), a Decision Tree and Random Forest, aNeural Network, a Clustering, a Visualization and a DimensionalityReduction, an Association Rule Learning, a Deep Belief Network, aReinforcement Learning, and a Deep learning algorithm, but the machinelearning network is not limited thereto and may include various learningalgorithms. Also, a subject to be concealed, e.g., a subject to beanonymized, may be personal information included in the original data x.Herein, the personal information may include any information related toa person, such as personal identification information, personal medicalinformation, personal biometric information, personal behavioralinformation, etc. As another example, the personal information mayinclude sensitive information or private information such as hardwardcircuit diagram, business secret, etc.

Then, the learning network 130 may apply a network operation to theinputted original data x using the learned parameters of the learningnetwork, to thereby output the characteristic information y on theoriginal data x. Herein, the characteristic information y may befeatures or logits of the original data x. Also, the characteristicinformation y may be feature values related to certain features in theoriginal data, or the logits including values related to at least one ofvectors, matrices, and coordinates related to the certain features. Forexample, if the original data are facial image data, the result abovemay be classes for face recognition, facial features, e.g., laughingexpressions, coordinates of facial landmark points, e.g., both endpoints on far sides of an eye.

Next, the data obfuscation device 100 may perform processes of updatingthe original data via backpropagation using at least part of (i) one ormore 1-st losses calculated by referring to the characteristicinformation y and its corresponding at least one 1-st ground truth, and(ii) one or more 2-nd losses calculated by referring to (ii-1) at leastone task specific output generated by using the characteristicinformation y and (ii-2) at least one 2-nd ground truth corresponding tothe task specific output, to thereby generate the obfuscated datacorresponding to the original data.

Herein, during the backpropagation using at least part of the 1-stlosses and the 2-nd losses, the data obfuscation device 100 may fix andnot update the learned parameters of the learning network 130, and mayperform backpropagation to minimize at least part of the 1-st losses andthe 2-nd losses for the original data only, to thereby obfuscate theoriginal data. Then, the obfuscated data may be recognized as datadifferent from the original data by a human, but may be recognized asdata similar or same as the original data by the learning network.

Also, during the backpropagation using at least part of the 1-st lossesand the 2-nd losses, the data obfuscation device 100 may acquire atleast one loss gradient for minimizing at least part of the 1-st lossesand the 2-nd losses, and may backpropagate the loss gradient to theoriginal data.

Meanwhile, the task specific output may be an output of a task to beperformed by the learning network 130, and may have various resultsaccording to the task learned by the learning network 130, such as aprobability of a class for classification, coordinates resulting fromregression for location detection, etc., and an activation function ofan activation unit may be applied to characteristic informationoutputted from the learning network 130, to thereby generate the taskspecific output according to the task to be performed by the learningnetwork 130. Herein, the activation function may include a sigmoidfunction, a linear function, a softmax function, an rlinear function(ReLU), a square function, a sqrt function, an srlinear function, an absfunction, a tank function, a brlinear function, etc. but the scope ofthe present disclosure is not limited thereto.

As one example, when the learning network 130 performs the task for theclassification, the processor 120 of the data obfuscation device 100 maymap the characteristic information outputted from the learning network130 onto each of classes, to thereby generate one or more probabilitiesof the original data, for each of the classes. Herein, the probabilitiesfor each of the classes may represent probabilities of thecharacteristic information, outputted for each of the classes from thelearning network 130, being correct. For example, if the original dataare the facial image data, a probability of the face having a laughingexpression may be outputted as 0.75, and a probability of the face nothaving the laughing expression may be outputted as 0.25, and the like.Herein, a softmax algorithm may be used for mapping the characteristicinformation outputted from the learning network 130 onto each of theclasses, but the scope of the present disclosure is not limited thereto,and various algorithms may be used for mapping the characteristicinformation onto each of the classes.

FIG. 3 is a drawing schematically illustrating a method for generatingthe 1-st obfuscated data to the n-th obfuscated data as the concealeddata. An output result calculated using the 1-st obfuscated data to then-th obfuscated data may be same or similar to an output resultcalculated using the original data, by using multiple learning networks130-1, 130-2, . . . , and 130-n having their own learned parameters inaccordance with the present disclosure. Herein, each of the multiplelearning networks 130-1, 130-2, . . . , and 130-n may have completedlearning to perform tasks at least part of which may be different fromeach other. In the description below, the part easily deducible from theexplanation of FIG. 2 will be omitted.

First, if the original data x is acquired, the data obfuscation device100 may perform processes of inputting the original data x into the 1-stlearning network 130-1, and allowing the 1-st learning network 130-1 to(i) apply a network operation to the original data using one or more1-st learned parameters of the 1-st learning network 130-1 and thus to(ii) output 1-st characteristic information y on the original data.

That is, according to the instructions stored in the memory 110 of thedata obfuscation device 100, if the original data x for concealment isacquired, the data obfuscation device 100, i.e., the processor 120, mayinput the original data x into the 1-st learning network 130-1.

Then, the 1-st learning network 130-1 may apply a network operation tothe inputted original data x using the 1-st learned parameters of the1-st learning network, to thereby output the 1-st characteristicinformation yl on the original data x.

Next, the data obfuscation device 100 may perform processes of updatingthe original data via backpropagation using at least part of (i) one ormore (1_1)-st losses calculated by referring to the 1-st characteristicinformation y and its corresponding at least one (1_1)-st ground truth,and (ii) one or more (2_1)-st losses calculated by referring to (ii-1)at least one 1-st task specific output generated by using the 1-stcharacteristic information yl and (ii-2) at least one (2_1)-st groundtruth corresponding to the 1-st task specific output, to therebygenerate 1-st obfuscated data corresponding to the original data.

Thereafter, while increasing an integer k from 2 to n, the dataobfuscation device 100 may repeat processes of (i) inputting (k-1)-thobfuscated data into a k-th learning network, and allowing the k-thlearning network to (i-1) apply a network operation to the (k-1)-thobfuscated data using one or more k-th learned parameters of the k-thlearning network and thus to (i-2) output k-th characteristicinformation on the (k-1)-th obfuscated data, and (ii) updating the(k-1)-th obfuscated data via backpropagation using at least part of(ii-1) one or more (1_k)-th losses calculated by referring to the k-thcharacteristic information and its corresponding at least one (1_k)-thground truth, and (ii-2) one or more 2_k)-th losses calculated byreferring to (ii-2a) at least one k-th task specific output generated byusing the k-th characteristic information and (ii-2b) at least one2_k)-th ground truth corresponding to the k-th task specific output, tothereby generate k-th obfuscated data corresponding to the (k-1)-thobfuscated data.

And as such, the data obfuscation device 100 may generate n-thobfuscated data by allowing the 2-nd learning network 130-2 to the n-thlearning network 130-n to respectively perform backpropagation with itsinput as previous obfuscated data created via backpropagation using eachprevious learning network, and thus, as a result of the repeatedprocesses, the n-th obfuscated data which are the concealed originaldata may be generated.

Also, the n-th obfuscated data are generated above as the obfuscateddata which are the concealed original data, however, the obfuscated datamay be generated by allowing the 1-st learning network 130-1 to the n-thlearning network 130-n to use the (1_1)-st losses to the (1_n)-th lossesand the (2_1)-st losses to the (2_n)-th losses to update the originaldata and conceal the original data.

That is, using the 1-st learning network 130-1 to the n-th learningnetwork 130-n as above, the data obfuscation device 100 may update theoriginal data via backpropagation using at least part of (i) a 1-staverage loss which is an average over the (1_1)-st losses to the(1_n)-th losses (ii) a 2-nd average loss which is an average over the(2_1)-st losses to the (2_n)-th losses and (iii) a 3-rd average losswhich is an average over a 1-st sum loss to an n-th sum loss whereineach of the 1-st sum loss to the n-th sum loss is each piecewisesummation of the (1_1)-st losses to the (1_n)-th losses and the (2_1)-stlosses to the (2_n)-th losses corresponding to the (1_1)-st losses tothe (1_n)-th losses, to thereby generate the obfuscated data which arethe concealed original data.

Meanwhile, the data obfuscation device 100 is shown above as generatingthe obfuscated data by allowing each of the 2-nd learning network 130-2to the n-th learning network 130-n to perform backpropagation with itsinput as previous obfuscated data created via backpropagation using eachprevious learning network, however, the original data x may be inputtedrespectively into the 1-st learning network 130-1 to the n-th learningnetwork 130-n, to thereby generate the obfuscated data with a singlebackpropagation.

That is, the data obfuscation device 100 may input the original data xinto each of the 1-st learning network 130-1 to the n-th learningnetwork 130-n, and allow each of the 1-st learning network 130-1 to then-th learning network 130-n to (i) apply its corresponding networkoperation to the original data x using respectively the 1-st learnedparameters to the n-th learned parameters of the 1-st learning network130-1 to the n-th learning network 130-n, and thus to (ii) output eachpiece of the 1-st characteristic information to the n-th characteristicinformation on the original data x using respectively the 1-st learnedparameters to the n-th learned parameters. Next, the data obfuscationdevice 100 may update the original data via backpropagation using the1-st loss which is an average over the (1_1)-st losses to the (1_n)-thlosses calculated by referring to (i) the 1-st characteristicinformation to the n-th characteristic information and (ii) the (1_1)-stground truth to the (1_n)-th ground truth corresponding respectively tothe 1-st characteristic information to the n-th characteristicinformation, to thereby generate the obfuscated data corresponding tothe original data.

Also, the data obfuscation device 100 may update the original data viabackpropagation using the 2-nd loss which is an average over the(2_1)-st losses to the (2_n)-th losses calculated by referring to (i)the 1-st task specific output to the n-th task specific output generatedby using each piece of the 1-st characteristic information to the n-thcharacteristic information and (ii) at least one (2_1)-st ground truthto at least one (2_n)-th ground truth respectively corresponding to the1-st task specific output to the n-th task specific output, to therebygenerate the obfuscated data corresponding to the original data.

In addition to this, the data obfuscation device 100 may update theoriginal data via backpropagation using the 3-rd loss which is anaverage over the 1-st sum loss to the n-th sum loss where each of the1-st sum loss to the n-th sum loss is each piecewise summation of the(1_1)-st losses to the (1_n)-th losses and the (2_1)-st losses to the(2_n)-th losses corresponding to the (1_1)-st losses to the (1_n)-thlosses, to thereby generate the obfuscated data corresponding to theoriginal data.

FIG. 4 is a drawing schematically illustrating a method for generatingthe obfuscated data as the concealed data, where the obfuscated data,corresponding to the original data, is created, with the modified dataas an input which is generated by modifying the original data, inaccordance with another example embodiment of the present disclosure. Inthe description below, the part easily deducible from the explanation ofFIG. 2 will be omitted.

First, if the original data x is acquired, the data obfuscation device100 may modify the original data x, to thereby generate the modifieddata x′.

Herein, the modified data x′ may be generated by adding at least onerandom noise created through a random noise generating network (notillustrated) to the original data x. As one example, the random noisegenerating network may be instructed to generate the random noise havinga normal distribution N(0, σ), and the generated noise may be added tothe original data x, to thereby generate the modified data x′. Also, themodified data x′ may be generated by blurring the original data x orchanging a resolution of the original data x, as well as using therandom noise, but the scope of the present disclosure is not limitedthereto, and various ways of modifying the original data may be used.

Next, the data obfuscation device 100 may input the modified data x′into the learning network 130, and allow the learning network 130 to (i)apply a network operation to the modified data using the learnedparameters of the learning network 130 and thus to (ii) outputcharacteristic information y on the modified data.

That is, according to the instructions stored in the memory 110 of thedata obfuscation device 100, if the original data x for concealment isacquired, the data obfuscation device 100, i.e., the processor 120, maymodify the original data x into the modified data x′ and then input themodified data x′ into the learning network 130.

Then, the learning network 130 may apply a network operation to theinputted modified data x′ using the learned parameters of the learningnetwork, to thereby output the characteristic information y on themodified data x′.

Next, the data obfuscation device 100 may perform processes of updatingthe original data or the modified data via backpropagation using atleast part of (i) the 1-st losses calculated by referring to thecharacteristic information y and its corresponding at least one 1-stground truth, and (ii) the 2-nd losses calculated by referring to (ii-1)the task specific output generated by using the characteristicinformation y and (ii-2) at least one 2-nd ground truth corresponding tothe task specific output, to thereby generate the obfuscated datacorresponding to the original data.

FIG. 5 is a drawing schematically illustrating a method for generatingthe 1-st obfuscated data to the n-th obfuscated data as the concealeddata, where the obfuscated data, corresponding to the original data, arecreated, with the modified data as an input which are generated bymodifying the original data, in accordance with another exampleembodiment of the present disclosure. In the description below, the parteasily deducible from the explanation of FIGS. 2 to 4 will be omitted.

First, if the original data x is acquired, the data obfuscation device100 may modify the original data x, to thereby generate the modifieddata x′.

Next, the data obfuscation device 100 may input the modified data x′into the 1-st learning network 130-1, and allow the 1-st learningnetwork 130-1 to (i) apply a network operation to the modified datausing the 1-st learned parameters of the 1-st learning network 130-1 andthus to (ii) output 1-st characteristic information y on the modifieddata.

That is, according to the instructions stored in the memory 110 of thedata obfuscation device 100, if the original data x for concealment isacquired, the data obfuscation device 100, i.e., the processor 120, maymodify the original data x into the modified data x′ and then input themodified data x′ into the 1-st learning network 130-1.

Then, the 1-st learning network 130-1 may apply a network operation tothe inputted modified data x′ using the 1-st learned parameters of the1-st learning network, to thereby output the 1-st characteristicinformation yl on the modified data x′.

Next, the data obfuscation device 100 may perform processes of updatingthe modified data via backpropagation using at least part of (i) one ormore (1_1)-st losses calculated by referring to the 1-st characteristicinformation y1 and its corresponding at least one (1_1)-st ground truth,and (ii) one or more (2_1)-st losses calculated by referring to (ii-1)at least one 1-st task specific output generated by using the 1-stcharacteristic information y1 and (ii-2) at least one (2_1)-st groundtruth corresponding to the 1-st task specific output, to therebygenerate 1-st obfuscated data corresponding to the modified data.

Thereafter, while increasing an integer k from 2 to n, the dataobfuscation device 100 may repeat processes of (i) inputting (k-1)-thobfuscated data into a k-th learning network, and allowing the k-thlearning network to (i-1) apply a network operation to the (k-1)-thobfuscated data using one or more k-th learned parameters of the k-thlearning network and thus to (i-2) output k-th characteristicinformation on the (k-1)-th obfuscated data, and (ii) updating the(k-1)-th obfuscated data via backpropagation using at least part of(ii-1) one or more (1_k)-th losses calculated by referring to the k-thcharacteristic information and its corresponding at least one (1_k)-thground truth, and (ii-2) one or more 2_k)-th losses calculated byreferring to (ii-2a) at least one k-th task specific output generated byusing the k-th characteristic information and (ii-2b) at least one2_k)-th ground truth corresponding to the k-th task specific output, tothereby generate k-th obfuscated data corresponding to the (k-1)-thobfuscated data.

And as such, the data obfuscation device 100 may generate n-thobfuscated data by allowing the 2-nd learning network 130-2 to the n-thlearning network 130-n to respectively perform backpropagation with itsinput as previous obfuscated data created via backpropagation using eachprevious learning network, and thus, as a result of the repeatedprocesses, the n-th obfuscated data which are the concealed originaldata may be generated.

Also, the n-th obfuscated data are generated above as the obfuscateddata which are the concealed original data, however, the obfuscated datamay be generated by allowing the 1-st learning network 130-1 to the n-thlearning network 130-n to use the (1_1)-st losses to the (1_n)-th lossesand the (2_1)-st losses to the (2_n)-th losses to update the originaldata or the modified data and conceal the original data.

That is, using the 1-st learning network 130-1 to the n-th learningnetwork 130-n as above, the data obfuscation device 100 may update theoriginal data or the modified data via backpropagation using at leastpart of (i) the 1-st average loss which is an average over the (1_1)-stlosses to the (1_n)-th losses (ii) the 2-nd average loss which is anaverage over the (2_1)-st losses to the (2_n)-th losses and (iii) the3-rd average loss which is an average over the 1-st sum loss to the n-thsum loss wherein each of the 1-st sum loss to the n-th sum loss is eachpiecewise summation of the (1_1)-st losses to the (1_n)-th losses andthe (2_1)-st losses to the (2_n)-th losses corresponding to the (1_1)-stlosses to the (1_n)-th losses, to thereby generate the obfuscated datawhich are the concealed original data.

Meanwhile, the data obfuscation device 100 is shown above as generatingthe obfuscated data by allowing each of the 2-nd learning network 130-2to the n-th learning network 130-n to respectively performbackpropagation with its input as previous obfuscated data created viabackpropagation using each previous learning network, however, themodified data x′ may be inputted respectively into the 1-st learningnetwork 130-1 to the n-th learning network 130-n, to thereby generatethe obfuscated data with a single backpropagation.

That is, the data obfuscation device 100 may input the modified data x′into each of the 1-st learning network 130-1 to the n-th learningnetwork 130-n, and allow each of the 1-st learning network 130-1 to then-th learning network 130-n to (i) apply its corresponding networkoperation to the modified data x′ using respectively the 1-st learnedparameters to the n-th learned parameters of the 1-st learning network130-1 to the n-th learning network 130-n, and thus to (ii) output eachpiece of the 1-st characteristic information to the n-th characteristicinformation on the modified data x′. Next, the data obfuscation device100 may update the original data or the modified via backpropagationusing the 1-st loss which is an average over the (1_1)-st losses to the(1_n)-th losses calculated by referring to (i) the 1-st characteristicinformation to the n-th characteristic information and (ii) the (1_1)-stground truth to the (1_n)-th ground truth corresponding respectively tothe 1-st characteristic information to the n-th characteristicinformation, to thereby generate the obfuscated data corresponding tothe original data.

Also, the data obfuscation device 100 may update the original data orthe modified data via backpropagation using the 2-nd loss which is anaverage over the (2_1)-st losses to the (2_n)-th losses calculated byreferring to (i) the 1-st task specific output to the n-th task specificoutput generated by using each piece of the 1-st characteristicinformation to the n-th characteristic information and (ii) at least one(2_1)-st ground truth to at least one (2_n)-th ground truth respectivelycorresponding to the 1-st task specific output to the n-th task specificoutput, to thereby generate the obfuscated data corresponding to theoriginal data.

In addition to this, the data obfuscation device 100 may update theoriginal data or the modified data via backpropagation using the 3-rdloss which is an average over the 1-st sum loss to the n-th sum losswhere each of the 1-st sum loss to the n-th sum loss is each piecewisesummation of the (1_1)-st losses to the (1_n)-th losses and the (2_1)-stlosses to the (2_n)-th losses corresponding to the (1_1)-st losses tothe (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.

FIG. 6 is a drawing schematically illustrating a method for concealingthe data in accordance with still another example embodiment of thepresent disclosure. In the description below, the part easily deduciblefrom the explanation of FIGS. 2 to 5 will be omitted.

First, if the original data x is acquired, the data obfuscation device100 may modify the original data x, to thereby generate the modifieddata x′.

Next, the data obfuscation device 100 may input the original data x intothe learning network 130, and allow the learning network 130 to (i)apply a network operation to the original data x using one or morelearned parameters of the learning network 130 and thus to (ii) output1-st characteristic information y on the original data. Also, the dataobfuscation device 100 may input the modified data x′ into the learningnetwork 130, and allow the learning network 130 to (i) apply a networkoperation to the modified data x′ using the learned parameters and thusto (ii) output 2-nd characteristic information y′ on the modified datax′.

Next, the data obfuscation device 100 may update the original data x orthe modified data x′ via backpropagation using the data lossescalculated by referring to at least part of (i) one or more 1-st lossescreated by referring to the 1-st characteristic information y and the2-nd characteristic information y′, and (ii) one or more 2-nd lossescreated by referring to (ii-1) at least one task specific outputgenerated by using the 2-nd characteristic information y′ and (ii-2) atleast one ground truth corresponding to the task specific output, tothereby generate the obfuscated data corresponding to the original data.

Herein, when calculating the 1-st losses by referring to the 1-stcharacteristic information and the 2-nd characteristic information, thedata obfuscation device 100 may calculate the 1-st losses by using adifference, e.g., a norm ∥y-y′∥, between the 1-st characteristicinformation and the 2-nd characteristic information, or may calculatethe 1-st losses by using a cosine similarity between the 1-stcharacteristic information and the 2-nd characteristic information, butthe scope of the present disclosure is not limited thereto.

Also, during the backpropagation using the data losses, the dataobfuscation device 100 may fix and not update the learned parameters ofthe learning network 130, and may perform backpropagation to minimize atleast part of the 1-st losses and the 2-nd losses for the original dataor the modified data only, to thereby obfuscate the original data. Then,the obfuscated data may be recognized as data different from theoriginal data by a human, but may be recognized as data similar or sameas the original data by the learning network.

Also, during the backpropagation using the data losses, the dataobfuscation device 100 may acquire at least one loss gradient forminimizing at least part of the 1-st losses and the 2-nd losses by usingthe data losses, and may backpropagate the loss gradient to the originaldata.

FIG. 7 is a drawing schematically illustrating a method for generatingthe 1-st obfuscated data to the n-th obfuscated data as the concealeddata. An output result calculated using the obfuscated data may be sameor similar to an output result calculated using the original data, byusing multiple learning networks 130-1, 130-2, . . . , and 130-n havingtheir own learned parameters in accordance with still another exampleembodiment of the present disclosure. In the description below, the parteasily deducible from the explanation of FIGS. 2 to 6 will be omitted.

First, if the original data x is acquired, the data obfuscation device100 may modify the original data x, to thereby generate the modifieddata x′.

Next, the data obfuscation device 100 may input the original data x intothe 1-st learning network 130-1, and allow the 1-st learning network130-1 to (i) apply a network operation to the original data x using the1-st learned parameters of the 1-st learning network 130-1 and thus to(ii) output (1_1)-st characteristic information yl on the original data.Also, the data obfuscation device 100 may input the modified data x′into the 1-st learning network 130-1, and allow the 1-st learningnetwork 130-1 to (i) apply a network operation to the modified data x′using the learned parameters and thus to (ii) output (2_1)-stcharacteristic information y1′ on the modified data x′.

Next, the data obfuscation device 100 may update the modified data x′via backpropagation using the 1-st data losses calculated by referringto at least part of (i) one or more (1_1)-st losses created by referringto the (1_1)-st characteristic information yl and the (2_1)-stcharacteristic information y1′, and (ii) one or more (2_1)-st lossescreated by referring to (ii-1) at least one 1-st task specific outputgenerated by using the (2_1)-st characteristic information y1′ and(ii-2) at least one 1-st ground truth corresponding to the 1-st taskspecific output, to thereby generate the 1-st obfuscated data.

Thereafter, while increasing integer k from 2 to n, the data obfuscationdevice 100 may repeat processes of (i) inputting the original data xinto the k-th learning network, and allowing the k-th learning networkto (i-1) apply a network operation to the original data x using the k-thlearned parameters of the k-th learning network and thus to (i-2) output(1_k)-th characteristic information yk on the original data, (ii)inputting (k-1)-th obfuscated data into the k-th learning network, andallowing the k-th learning network to (ii-1) apply a network operationto the (k-1)-th obfuscated data using the k-th learned parameters of thek-th learning network and thus to (ii-2) output 2_k)-th characteristicinformation yk′ on the (k-1)-th obfuscated data, and (iii) updating the(k-1)-th obfuscated data via backpropagation using one or more k-th datalosses calculated by referring to at least part of (iii-1) one or more(1_k)-th losses created by referring to the (1_k)-th characteristicinformation yk and the 2_k)-th characteristic information yk′, and(iii-2) one or more 2_k)-th losses created by referring to (iii-2a) atleast one k-th task specific output generated by using the 2_k)-thcharacteristic information yk′ and (iii-2b) at least one k-th groundtruth corresponding to the k-th task specific output, to therebygenerate the k-th obfuscated data corresponding to the (k-1)-thobfuscated data, and thus, as a result of the repeated processes, then-th obfuscated data, which are the obfuscated data corresponding to theoriginal data, may be generated.

Also, the n-th obfuscated data are generated above as the obfuscateddata which are the concealed original data, however, the obfuscated datamay be generated by concealing the original data through updating theoriginal data or the modified data via allowing the 1-st learningnetwork 130-1 to the n-th learning network 130-n to use the 1-st datalosses to the n-th data losses.

That is, via the processes above, the data obfuscation device 100 may(i) acquire the 1-st data losses to the n-th data losses through the1-st learning network 130-1 and the n-th learning network 130-n, and(ii) update the original data or the modified data via backpropagationusing at least one average data loss which is an average over theacquired 1-st data losses to the acquired n-th data losses, to therebygenerate the obfuscated data which are the concealed original data.

Also, the original data are shown above as inputted respectively intothe 1-st learning network 130-1 to the n-th learning network 130-n, tothereby acquire the characteristic information on the original data, andthe (1_1)-st losses to the (1_n)-th losses are shown as calculated usingthe characteristic information, however, the original data may beinputted into at least one specific learning network among the 1-stlearning network 130-1 to the n-th learning network 130-n, to therebyacquire specific characteristic information on the original data, andthe original data or the modified data may be updated using the specificcharacteristic information.

As one example, the data obfuscation device 100 may allow the at leastone specific learning network among the 1-st learning network 130-1 tothe n-th learning network 130-n to (i) apply its corresponding networkoperation to the original data x using one or more specific learnedparameters of the specific learning network and thus to (ii) output thespecific characteristic information on the original data.

And, the data obfuscation device 100 may (i) input the modified data x′into the 1-st learning network 130-1, and allow the 1-st learningnetwork 130-1 to (i-1) apply a network operation to the modified datausing the 1-st learned parameters, and thus to (i-2) output (2_1)-stcharacteristic information on the modified data, (ii) calculate the(1_1)-st losses by referring to one or more 1-st probabilities of themodified data and at least one ground truth where the 1-st probabilitiesof the modified data are generated by mapping the (2_1)-stcharacteristic information onto each of the classes and where the groundtruth corresponds to the original data, and (iii) update the modifieddata via backpropagation using the (1_1)-st losses, to thereby generatethe 1-st obfuscated data corresponding to the modified data. And, whileincreasing an integer k from 2 to n, the data obfuscation device 100 mayrepeat processes of (i) inputting the (k-1)-th obfuscated data into thek-th learning network, and allowing the k-th learning network to (i-1)apply a network operation to the (k-1)-th obfuscated data using the k-thlearned parameters of the k-th learning network and thus to (i-2) output2_k)-th characteristic information on the (k-1)-th obfuscated data, (ii)calculating the (1_k)-th losses by referring to one or more k-thprobabilities of the (k-1)-th obfuscated data and at least one groundtruth corresponding to the original data where the k-th probabilities ofthe (k-1)-th obfuscated data are generated by mapping the 2_k)-thcharacteristic information onto each of the classes, and (iii) updatingthe (k-1)-th obfuscated data via backpropagation using the (1_k)-thlosses, to thereby generate the k-th obfuscated data corresponding tothe (k-1)-th obfuscated data. And thus, as a result of the repeatedprocesses, the n-th obfuscated data may be generated.

Thereafter, the data obfuscation device 100 may update the original dataor the modified data, by using at least part of (i) one or more 1-st sublosses calculated by referring to the the (1_1)-st losses to the(1_n)-th losses and (ii) one or more 2-nd sub losses calculated byreferring to (ii-1) the specific characteristic information and (ii-2)the (2_1)-st characteristic information to the (2_n)-th characteristicinformation, to thereby generate the obfuscated data corresponding tothe original data.

Herein, the data obfuscation device 100 may (i) calculate, as the 1-stsub losses, at least one average loss which is an average over the(1_1)-st losses to the (1_n)-th losses, and (ii) calculate the 2-nd sublosses by referring to the specific characteristic information and anaverage over the (2_1)-st characteristic information to the (2_n)-thcharacteristic information.

Meanwhile, the data obfuscation device 100 is shown above as generatingthe obfuscated data by allowing each of the 2-nd learning network 130-2to the n-th learning network 130-n to respectively performbackpropagation with its input as previous obfuscated data created viabackpropagation using each previous learning network, however, themodified data x′ may be inputted respectively into the 1-st learningnetwork 130-1 to the n-th learning network 130-n, to thereby generatethe obfuscated data with a single backpropagation.

That is, the data obfuscation device 100 may input the original data xinto each of the 1-st learning network 130-1 to the n-th learningnetwork 130-n, and allow each of the 1-st learning network 130-1 to then-th learning network 130-n to (i) apply its corresponding networkoperation to the original data x using respectively the 1-st learnedparameters to the n-th learned parameters of the 1-st learning network130-1 to the n-th learning network 130-n, and thus to (ii) output eachpiece of the (1_1)-st characteristic information yl to the (1_n)-thcharacteristic information yn on the original data x. Also, the dataobfuscation device 100 may input the modified data x′ into each of the1-st learning network 130-1 to the n-th learning network 130-n, andallow each of the 1-st learning network 130-1 to the n-th learningnetwork 130-n to (i) apply its corresponding network operation to themodified data x′ using respectively the 1-st learned parameters to then-th learned parameters of the 1-st learning network 130-1 to the n-thlearning network 130-n, and thus to (ii) output each piece of the(2_1)-st characteristic information y1′ to the (2_n)-th characteristicinformation yn′ on the modified data x′ using respectively the 1-stlearned parameters to the n-th learned parameters.

And, the data obfuscation device 100 may update the original data or themodified via backpropagation using the 1-st loss which is an averageover the (1_1)-st losses to the (1_n)-th losses calculated by referringto (i) the (1_1)-st characteristic information to the (1_n)-thcharacteristic information and (ii) the (2_1)-st characteristicinformation to the (2_n)-th characteristic information correspondingrespectively to the (1_1)-st characteristic information to the (1_n)-thcharacteristic information, to thereby generate the obfuscated datacorresponding to the original data.

Also, the data obfuscation device 100 may update the original data orthe modified data via backpropagation using the 2-nd loss which is anaverage over the (2_1)-st losses to the (2_n)-th losses calculated byreferring to (i) the 1-st task specific output to the n-th task specificoutput generated by using each piece of the (2_1)-st characteristicinformation to the (2_n)-th characteristic information and (ii) at leastone 1-st ground truth to at least one n-th ground truth respectivelycorresponding to the 1-st task specific output to the n-th task specificoutput, to thereby generate the obfuscated data corresponding to theoriginal data.

In addition to this, the data obfuscation device 100 may update theoriginal data or the modified data via backpropagation using the 3-rdloss which is an average over the 1-st sum loss to the n-th sum losswhere each of the 1-st sum loss to the n-th sum loss is each piecewisesummation of the (1_1)-st losses to the (1_n)-th losses and the (2_1)-stlosses to the (2_n)-th losses corresponding to the (1_1)-st losses tothe (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.

FIGS. 8A and 8B are drawings schematically illustrating the originaldata and the concealed original data in accordance with the presentdisclosure.

FIG. 8A is a drawing exemplarily illustrating 64 image samples selectedfrom the CIFAR-10 dataset which includes images collected and labeled byCanadian Institute for Advanced Research (CIFAR) for imageclassification.

The obfuscated data generated by concealing the image samples, as theoriginal data in FIG. 8A in accordance with example embodiments of thepresent disclosure, are shown FIG. 8B.

By referring to FIGS. 8A and 8B, the 64 obfuscated data in FIG. 8B whichare concealed in accordance with the present disclosure are visuallydifferent from the 64 original data in FIG. 8A, but if the 64 obfuscateddata are inputted into the learning network, the learning networkoutputs a result same or similar to that of the original data.

Meanwhile, the obfuscated data which are concealed by the dataobfuscation device 100 in accordance with example embodiments of thepresent disclosure may be provided or sold to a buyer of image big data.

Also, in accordance with example embodiments of the present disclosure,when the concealed image data are provided or sold to the buyer, themethod of the data obfuscation device 100 may be provided as implementedin a form of program instructions executable by a variety of computercomponents and recorded to computer readable media. In accordance withone example embodiment of the present disclosure, the buyer may executethe program instructions recorded in the computer readable media byusing the computer devices, to thereby generate concealed data fromoriginal data owned by the buyer or acquired from other sources, and usethe concealed data for his/her own learning network. Also, the buyer mayuse at least two of the concealed data, the original image data owned bythe buyer or acquired from other sources, and the concealed image dataprovided or sold to the buyer, together for the buyer's learningnetwork. Meanwhile, in accordance with one example embodiment of thepresent disclosure, if the method of the data obfuscation device 100 isimplemented as the program instructions that can be executed by avariety of computer components, then computational overhead may occur inthe computing devices of the buyer when the accuracy is set as high,thus the buyer is allowed to lower the accuracy to prevent thecomputational overhead.

Meanwihle, the “average” mentioned in this specification may represent aweighted average but it is not limited thereto.

The present disclosure has an effect of performing concealment in asimple and accurate way, since processes of finding personalidentification information in data are eliminated.

The present disclosure has another effect of protecting privacy andsecurity of the original data by generating irreversibly obfuscated andconcealed data from the original data.

The present disclosure has still another effect of generating datarecognized as similar or same by a computer but recognized as differentby a human.

The present disclosure has still yet another effect of stimulating a bigdata trade market.

The embodiments of the present disclosure as explained above can beimplemented in a form of executable program command through a variety ofcomputer means recordable in computer readable media. The computerreadable media may include solely or in combination, program commands,data files, and data structures. The program commands recorded to themedia may be components specially designed for the present disclosure ormay be usable to those skilled in the art of computer software. Computerreadable media include magnetic media such as hard disk, floppy disk,and magnetic tape, optical media such as CD-ROM and DVD, magneto-opticalmedia such as floptical disk and hardware devices such as ROM, RAM, andflash memory specially designed to store and carry out program commands.Program commands may include not only a machine language code made by acomplier but also a high level code that can be used by an interpreteretc., which may be executed by a computer. The aforementioned hardwaredevice can work as more than a software module to perform the action ofthe present disclosure and vice versa.

As seen above, the present disclosure has been explained by specificmatters such as detailed components, limited embodiments, and drawings.They have been provided only to help more general understanding of thepresent disclosure. It, however, will be understood by those skilled inthe art that various changes and modification may be made from thedescription without departing from the spirit and scope of thedisclosure as defined in the following claims.

Accordingly, the thought of the present disclosure must not be confinedto the explained embodiments, and the following patent claims as well aseverything including variations equal or equivalent to the patent claimspertain to the category of the thought of the present disclosure.

What is claimed is:
 1. A method for concealing original data to protectpersonal information, a concealed data being recognized as similar orsame as the original data by a computer but different by a human,comprising steps of: (a) a data obfuscation device, on condition thatthe original data is acquired, inputting the original data or itsmodified data into a learning network, and allowing the learning networkto (i) apply a network operation to the original data or the modifieddata using one or more learned parameters of the learning network andthus to (ii) output characteristic information on the original data orthe modified data, the characteristic information being at least one offeatures and logits of the original data or modified data; and (b) thedata obfuscation device updating the original data or the modified datavia backpropagation using at least part of (i) one or more 1-st lossescalculated by referring to the characteristic information and itscorresponding at least one 1-st ground truth, and (ii) one or more 2-ndlosses calculated by referring to (ii-1) at least one task specificoutput generated by using the characteristic information and (ii-2) atleast one 2-nd ground truth corresponding to the task specific output,to thereby generate obfuscated data corresponding to the original data,wherein (b) includes maintaining the learned parameters during thebackpropagation regardless of resultant calculated losses, wherein thelearning network includes a 1-st learning network to an n-th learningnetwork respectively having one or more 1-st learned parameters to oneor more n-th learned parameters wherein n is an integer greater than 0,wherein, at the step of (a), the data obfuscation device inputs theoriginal data or the modified data into each of the 1-st learningnetwork to the n-th learning network, and allows each of the 1-stlearning network to the n-th learning network to (i) apply itscorresponding network operation to the original data or the modifieddata using respectively the 1-st learned parameters to the n-th learnedparameters of the 1-st learning network to the n-th learning network,and thus to (ii) output each piece of 1-st characteristic information ton-th characteristic information on the original data or the modifieddata, and wherein, at the step of (b), the data obfuscation deviceupdates the original data or the modified data via backpropagation usingat least part of (i) one of the 1-st losses which is an average over(1_1)-st losses to (1_n)-th losses wherein the (1_1)-st losses to the(1_n)-th losses are calculated by referring to the 1-st characteristicinformation to the n-th characteristic information and at least one(1_1)-st ground truth to at least one (1_n)-th ground truth respectivelycorresponding to the 1-st characteristic information to the n-thcharacteristic information, (ii) one of the 2-nd losses which is anaverage over (2_1)-st losses to (2_n)-th losses wherein the (2_1)-stlosses to the (2_n)-th losses are calculated by referring to a 1-st taskspecific output to an n-th task specific output generated by using eachpiece of the 1-st characteristic information to the n-th characteristicinformation and by further referring to at least one (2_1)-st groundtruth to at least one (2_n)-th ground truth respectively correspondingto the 1-st task specific output to the n-th task specific output, and(iii) a 3-rd loss which is an average over a 1-st sum loss to an n-thsum loss wherein each of the 1-st sum loss to the n-th sum loss is eachpiecewise summation of the (1_1)-st losses to the (1_n)-th losses andthe (2_1)-st losses to the (2_n)-th losses corresponding to the (1_1)-stlosses to the (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.
 2. The method of claim 1, wherein,at the step of (b), the data obfuscation device acquires at least oneloss gradient for minimizing at least part of the 1-st losses and the2-nd losses, and backpropagates the loss gradient to the original dataor the modified data.
 3. The method of claim 1, wherein, at the step of(a), the data obfuscation device generates the modified datacorresponding to the original data by performing at least one of aprocess of adding a random noise created by a random noise generatingnetwork to the original data, a process of blurring the original dataand, a process of changing a resolution of the original data.
 4. Amethod for concealing original data to protect personal information, aconcealed data being recognized as similar or same by a computer butdifferent by a human, comprising steps of: (a) a data obfuscationdevice, on condition that the original data is acquired, modifying theoriginal data, to thereby generate modified data; (b) the dataobfuscation device, (i) inputting the original data into a learningnetwork, and allowing the learning network to (i-1) apply a networkoperation to the original data using one or more learned parameters ofthe learning network and thus to (i-2) output 1-st characteristicinformation on the original data, the 1-st characteristic informationbeing at least one of features and logits of the original data, and (ii)inputting the modified data into the learning network, and allowing thelearning network to (ii-1) apply a network operation to the modifieddata using the learned parameters and thus to (ii-2) output 2-ndcharacteristic information on the modified data, the 2-nd characteristicinformation being at least one of features and logits of the modifieddata; and (c) the data obfuscation device updating the original data orthe modified data via backpropagation using one or more data lossescreated by referring to at least part of (i) one or more 1-st lossescalculated by referring to the 1-st characteristic information and the2-nd characteristic information, and (ii) one or more 2-nd lossescalculated by referring to (ii-1) at least one task specific outputgenerated by using the 2-nd characteristic information and (ii-2) atleast one ground truth corresponding to the task specific output, tothereby generate obfuscated data corresponding to the original data,wherein (c) includes maintaining the learned parameters during thebackpropagation regardless of resultant calculated losses, wherein thelearning network includes a 1-st learning network to an n-th learningnetwork respectively having one or more 1-st learned parameters to oneor more n-th learned parameters wherein n is an integer greater than 0,wherein, at the step of (b), the data obfuscation device (i) inputs theoriginal data and the modified data into each of the 1-st learningnetwork to the n-th learning network, and (ii) allows each of the 1-stlearning network to the n-th learning network to (ii-1) apply itscorresponding network operation to the original data and the modifieddata using respectively the 1-st learned parameters to the n-th learnedparameters of the 1-st learning network to the n-th learning network,thus to (ii-2) output each piece of (1_1)-st characteristic informationto (1_n)-th characteristic information on the original data, and (ii-3)output each piece of (2_1)-st characteristic information to (2_n)-thcharacteristic information on the modified data, and wherein, at thestep of (c), the data obfuscation device updates the original data orthe modified data via backpropagation using at least part of (i) one ofthe 1-st losses which is an average over (1_1)-st losses to (1_n)-thlosses wherein the (1_1)-st losses to the (1_n)-th losses are calculatedby referring to the (1_1)-st characteristic information to the (1_n)-thcharacteristic information and the (2_1)-st characteristic informationto the (2_n)-th characteristic information corresponding to the (1_1)-stcharacteristic information to the (1_n)-th characteristic information,(ii) one of the 2-nd losses which is an average over (2_1)-st losses to(2_n)-th losses wherein the (2_1)-st losses to the (2_n)-th losses arecalculated by referring to a 1-st task specific output to an n-th taskspecific output generated by using each piece of the (2_1)-stcharacteristic information to the (2_n)-th characteristic informationand by further referring to at least one 1-st ground truth to at leastone n-th ground truth respectively corresponding to the 1-st taskspecific output to the n-th task specific output, and (iii) a 3-rd losswhich is an average over a 1-st sum loss to an n-th sum loss whereineach of the 1-st sum loss to the n-th sum loss is each piecewisesummation of the (1_1)-st losses to the (1_n)-th losses and the (2_1)-stlosses to the (2_n)-th losses corresponding to the (1_1)-st losses tothe (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.
 5. The method of claim 4, wherein,at the step of (a), the data obfuscation device generates the modifieddata corresponding to the original data by performing at least one of aprocess of adding a random noise created by a random noise generatingnetwork to the original data, a process of blurring of the originaldata, and a process of changing a resolution of the original data.
 6. Adata obfuscation device for concealing original data to protect personalinformation, a concealed data being recognized as similar or same as theoriginal data by a computer but different by a human, comprising: atleast one memory that stores instructions; and at least one processorconfigured to execute the instructions to perform or support anotherdevice to perform processes of: (I) on condition that the original datais acquired, inputting the original data or its modified data into alearning network, and allowing the learning network to (i) apply anetwork operation to the original data or the modified data using one ormore learned parameters of the learning network and thus to (ii) outputcharacteristic information on the original data or the modified data,the characteristic information being at least one of features and logitsof the original data or modified data, and (II) updating the originaldata or the modified data via backpropagation using at least part of (i)one or more 1-st losses calculated by referring to the characteristicinformation and its corresponding at least one 1-st ground truth, and(ii) one or more 2-nd losses calculated by referring to (ii-1) at leastone task specific output generated by using the characteristicinformation and (ii-2) at least one 2-nd ground truth corresponding tothe task specific output, to thereby generate obfuscated datacorresponding to the original data, wherein (b) includes maintaining thelearned parameters during the backpropagation regardless of resultantcalculated losses, wherein the learning network includes a 1-st learningnetwork to an n-th learning network respectively having one or more 1-stlearned parameters to one or more n-th learned parameters wherein n isan integer greater than 0, wherein, at the process of (I), the processorinputs the original data or the modified data into each of the 1-stlearning network to the n-th learning network, and allows each of the1-st learning network to the n-th learning network to (i) apply itscorresponding network operation to the original data or the modifieddata using respectively the 1-st learned parameters to the n-th learnedparameters of the 1-st learning network to the n-th learning network,and thus to (ii) output each piece of 1-st characteristic information ton-th characteristic information on the original data or the modifieddata, and wherein, at the process of (II), the processor updates theoriginal data or the modified data via backpropagation using at leastpart of (i) one of the 1-st losses which is an average over (1_1)-stlosses to (1_n)-th losses wherein the (1_1)-st losses to the (1_n)-thlosses are calculated by referring to the 1-st characteristicinformation to the n-th characteristic information and at least one(1_1)-st ground truth to at least one (1_n)-th ground truth respectivelycorresponding to the 1-st characteristic information to the n-thcharacteristic information, (ii) one of the 2-nd losses which is anaverage over (2_1)-st losses to (2_n)-th losses wherein the (2_1)-stlosses to the (2_n)-th losses are calculated by referring to a 1-st taskspecific output to an n-th task specific output generated by using eachpiece of the 1-st characteristic information to the n-th characteristicinformation and by further referring to at least one (2_1)-st groundtruth to at least one (2_n)-th ground truth respectively correspondingto the 1-st task specific output to the n-th task specific output, and(iii) a 3-rd loss which is an average over a 1-st sum loss to an n-thsum loss wherein each of the 1-st sum loss to the n-th sum loss is eachpiecewise summation of the (1_1)-st losses to the (1_n)-th losses andthe (2_1)-st losses to the (2_n)-th losses corresponding to the (1_1)-stlosses to the (1_n)-th losses, to thereby generate the obfuscated datacorresponding to the original data.
 7. The data obfuscation device ofclaim 6, wherein, at the process of (II), the processor acquires atleast one loss gradient for minimizing at least part of the 1-st lossesand the 2-nd losses, and backpropagates the loss gradient to theoriginal data or the modified data.
 8. The data obfuscation device ofclaim 6, wherein, at the process of (I), the processor generates themodified data corresponding to the original data by performing at leastone of a process of adding a random noise created by a random noisegenerating network to the original data, a process of blurring theoriginal data and, a process of changing a resolution of the originaldata.
 9. A data obfuscation device for concealing original data toprotect personal information, a concealed data being recognized assimilar or same as the original data by a computer but different by ahuman, comprising: at least one memory that stores instructions; and atleast one processor configured to execute the instructions to perform orsupport another device to perform processes of: (I) on condition thatthe original data is acquired, modifying the original data, to therebygenerate modified data, (II) (i) inputting the original data into alearning network, and allowing the learning network to (i-1) apply anetwork operation to the original data using one or more learnedparameters of the learning network and thus to (i-2) output 1-stcharacteristic information on the original data, the 1-st characteristicinformation being at least one of features and logits of the originaldata, and (ii) inputting the modified data into the learning network,and allowing the learning network to (ii-1) apply a network operation tothe modified data using the learned parameters and thus to (ii-2) output2-nd characteristic information on the modified data, the 2-ndcharacteristic information being at least one of features and logits ofthe modified data, and (III) updating the original data or the modifieddata via backpropagation using one or more data losses created byreferring to at least part of (i) one or more 1-st losses calculated byreferring to the 1-st characteristic information and the 2-ndcharacteristic information, and (ii) one or more 2-nd losses calculatedby referring to (ii-1) at least one task specific output generated byusing the 2-nd characteristic information and (ii-2) at least one groundtruth corresponding to the task specific output, to thereby generateobfuscated data corresponding to the original data, wherein (III)includes maintaining the learned parameters during the backpropagationregardless of resultant calculated losses, wherein the learning networkincludes a 1-st learning network to an n-th learning networkrespectively having one or more 1-st learned parameters to one or moren-th learned parameters wherein n is an integer greater than 0, wherein,at the process of (II), the processor (i) inputs the original data andthe modified data into each of the 1-st learning network to the n-thlearning network, and (ii) allows each of the 1-st learning network tothe n-th learning network to (ii-1) apply its corresponding networkoperation to the original data and the modified data using respectivelythe 1-st learned parameters to the n-th learned parameters of the 1-stlearning network to the n-th learning network, thus to (ii-2) outputeach piece of (1_1)-st characteristic information to (1_n)-thcharacteristic information on the original data, and (ii-3) output eachpiece of (2_1)-st characteristic information to (2_n)-th characteristicinformation on the modified data, and wherein, at the process of (III),the processor updates the original data or the modified data viabackpropagation using at least part of (i) one of the 1-st losses whichis an average over (1_1)-st losses to (1_n)-th losses wherein the(1_1)-st losses to the (1_n)-th losses are calculated by referring tothe (1_1)-st characteristic information to the (1_n)-th characteristicinformation and the (2_1)-st characteristic information to the (2_n)-thcharacteristic information corresponding to the (1_1)-st characteristicinformation to the (1_n)-th characteristic information, (ii) one of the2-nd losses which is an average over (2_1)-st losses to (2_n)-th losseswherein the (2_1)-st losses to the (2_n)-th losses are calculated byreferring to a 1-st task specific output to an n-th task specific outputgenerated by using each piece of the (2_1)-st characteristic informationto the (2_n)-th characteristic information and by further referring toat least one 1-st ground truth to at least one n-th ground truthrespectively corresponding to the 1-st task specific output to the n-thtask specific output, and (iii) a 3-rd loss which is an average over a1-st sum loss to an n-th sum loss wherein each of the 1-st sum loss tothe n-th sum loss is each piecewise summation of the (1_1)-st losses tothe (1_n)-th losses and the (2_1)-st losses to the (2_n)-th lossescorresponding to the (1_1)-st losses to the (1_n)-th losses, to therebygenerate the obfuscated data corresponding to the original data.
 10. Thedata obfuscation device of claim 9, wherein, at the process of (I), theprocessor generates the modified data corresponding to the original databy performing at least one of a process of adding a random noise createdby a random noise generating network to the original data, a process ofblurring of the original data, and a process of changing a resolution ofthe original data.